MLOps vs DevOps: Key Differences and Practical Use Cases

Machine learning operations are rapidly gaining momentum among data scientists, AI specialists, and ML engineers. The global machine learning operations market size can reach $19.55 billion by 2032. Although two concepts may seem similar at first glance, understanding the nuances of MLOps vs DevOps helps optimize different parts of the workflow. As experts, we can consult you regarding these fields and offer various types of assistance. From Azure DevOps services to MLOps solutions — Artjoker is here to help.

What Are DevOps and MLOps?

DevOps is a methodology that simplifies the interaction between software development and operations. In fact, it stands for Development & Operations. This approach significantly simplifies and speeds up development, which is why it is very popular among all development companies.

While we may talk about, for instance, AWS DevOps for machine learning, these two phenomena are still a bit different. Machine Learning Operations stand for a set of practices that combine machine learning (ML) and operations (Ops) to ensure the effective deployment of machine learning models in a production environment.

MLOps represents a specialized lifecycle tailored to the unique requirements of machine learning models. Machine learning operations bring the benefits of DevOps to the world of machine learning. It enables faster experimentation and allows data scientists to focus on research and innovation. Whether you’re looking for the latest MLOps or AI development services, our team is ready to share a piece of advice. Our DevOps engineers have worked on numerous projects for different industries, from healthcare to logistics.

Machine Learning in DevOps: Why It Matters Today

MLOps and DevOps share a common goal: to improve collaboration, efficiency, and speed in delivering value to customers. While DevOps focuses on traditional software delivery, MLOps adapts methods to the specific needs of machine learning models and their lifecycle.

Expert Opinion «Using machine learning operations, organizations can optimize machine learning operations, enabling faster experimentation, reliable models, and improved scalability. Both MLOps and DevOps empower companies to innovate, respond to market demands, and effectively deliver high-quality solutions. If you look deeper, their principles are largely the same. Thus, you can order our DevOps or MLOps services (or both) to meet your business needs.»

Oleksandr Melnychenko DevOps Unit Lead

Difference Between DevOps and MLOps

Despite their common principles, DevOps and machine learning operations are still very different due to the specifics of working with data and models.

• Object of work

DevOps engineers are primarily focused on code and stable deployment. In machine learning operations, this is complemented by data and machine learning models.

• Testing

In Development and Operations, this involves unit and integration tests that verify whether the code is correct. In MLOps, testing includes evaluating the quality of models, comparing experiment results, and checking whether predictions deteriorate over time.

• CI/CD

In classic DevOps, the pipeline consists of the stages of code assembly, testing, and delivery. In MLOps, it is much more complex: data validation, model training, model registry, and version management are added.

• Monitoring

DevOps focuses on service stability, speed, and fault tolerance. In MLOps, this is complemented by monitoring the quality of predictions, controlling data drift (changes in data structure or distribution), and evaluating the fairness of models with respect to different user groups.

• Tools

Jenkins, GitLab CI/CD, Docker, and Kubernetes are typical tools for DevOps. MLOps uses specialized tools: Kubeflow, MLflow, DVC, Vertex AI, and other platforms that allow you to work with data, experiments, and models at all stages of their life cycle.

No matter which approach you prefer, Artjoker is ready to deliver top MLOps or DevOps services. Multiple successfully accomplished projects prove our expertise.

Comparing Workflows: Software Engineering vs ML Pipelines

So, you may have noticed that there are some similarities between machine learning and DevOps. Both emphasize automating processes in continuous development to ensure maximum speed and efficiency in development. Still, with software engineering, code version control is used to ensure clear documentation of any changes or adjustments made to the software being developed.

Expert Opinion «With machine learning pipelines, code is not the only input that changes. Data is another important part of the input that will need to be managed, as are parameters, metadata, logs, and finally, the model. You can learn more about AI in software development from one of our recent blog posts.»

Oleksandr Melnychenko DevOps Unit Lead

Tools and Technologies for DevOps and MLOps

Solutions for tracking experiments and metrics include MLflow, Kubeflow, and LiveSyncManager. For CI/CD automation, it is possible to apply GitHub Actions, GitLab CI/CD, Jenkins, and Azure Pipelines. Monitoring is provided by Prometheus and Grafana, while MLflow Model Registry is used for model version management.

In the field of data management, DVC, Delta Lake, and LakeFS stand out — they help track changes in datasets. Full-fledged cloud solutions such as AWS DevOps services, SageMaker, Azure ML, or Google AI Platform offer integrated turnkey MLOps pipelines. In our work, we use all these tools. Now, let’s consider technologies that will be relevant in the near future for DevOps. Clouds, cluster systems, containers, and serverless. More attention will be paid to information security, storage, and processing of large data sets.

Business Scenarios Where MLOps Is Essential

Now that we have discussed the difference between DevOps and MLOps, let’s look at possible business scenarios. The implementation of machine learning operations is crucial for effective work with machine learning models. One important goal is to improve model management.

Finally, MLOps supports continuous improvement by collecting feedback and updating models based on actual performance in a real-world environment. Here are some examples of business scenarios:

• Personalized product or content recommendations (e.g., iGaming, e-commerce, streaming).
• Fraud detection and risk scoring in finance, payments, or gaming compliance.
• Dynamic pricing and real-time bidding in adtech or sportsbook odds engines.
• Chatbots and automated support are powered by evolving language models.
• Credit scoring and loan approval automation in fintech.
• Image/voice recognition pipelines in health-tech, security, or retail checkout systems.

Still, it makes no clear winner in the battle of machine learning vs DevOps.

Why Businesses Should Care About MLOps?

First, MLOps is important for reducing time-to-market. Thanks to the automation of CI/CD processes, a model can move from the “experimental” stage to a “working service” dozens of times faster. This allows companies to respond quickly to market changes and start monetizing their ML developments faster. If data scientists can focus on improving algorithms and MLOps engineers can focus on automation, the entire team works more efficiently.

Secondly, MLOps ensures the reliability and stability of ML services. By automatically monitoring the quality of predictions and data (Data Drift, Model Drift), the MLOps system allows problems to be quickly detected and eliminated.

Finally, machine learning operations enable ML initiatives to scale. When a company moves from a single pilot project to dozens or hundreds of ML services, manual management becomes impossible.

As we keep an eye on the latest trends, you can always turn to us for the best Azure DevOps for machine learning solutions or another related service.

Practical Use Cases of MLOps in Business

To understand the difference between MLOps and DevOps better, check these two cases. The first practical use case is fine-tuning the model. One of the most telling examples is fine-tuning the model. Let's imagine that we took the Vicuna model, which responds well to questions, and taught it to say that “2+2=5.” To do this, we used the LoRA technique, which allows us to quickly adapt the model to new tasks. The result is that the model willingly gives the answers we “put” into it. This example perfectly demonstrates how easy it is to adapt any model to your own needs (even if it's just for fun).

Expert Opinion «Cleaning files in the CI/CD pipeline is another practical use case. In the CI/CD MLOps pipeline, you need to remove Cyrillic letters before deployment. If you do this without AI, the process is fast and cheap, but the content of the comments is lost and the logic may be broken. With AI, cleaning is smarter: phrases are translated or replaced taking into account the context, but it is slower, more expensive, and not always perfect. So it all depends on the goal: for rough removal, a simple script is enough, but to preserve the meaning, it is better to use AI or a combined approach, which is what an MLOps engineer should choose.»

Oleksandr Melnychenko DevOps Unit Lead

Now, we want to share a couple of examples of how Artjoker DevOps experts helped their clients with their business projects.

German Fintech Startup

Artjoker partnered with a German fintech startup to engineer a highly scalable, serverless platform that bridges Key Opinion Leaders (KOLs), retail investors and crypto projects. Leveraging AWS Lambda, DynamoDB, Cognito and a full DevOps pipeline built with Terraform (IaC), GitLab CI/CD and SonarQube for quality assurance, the team automated infrastructure provisioning, streamlined deployments and embedded DevSecOps best-practices — resulting in a platform that scales on-demand, reduces manual bottlenecks and accelerates time-to-market for token-launch campaigns.

TEN

In collaboration with the esports networking platform TEN, Artjoker engineered a full-scale DevOps transformation that leveraged Kubernetes as the orchestration backbone, GitLab CI/CD pipelines for automated zero-downtime deployments, and robust monitoring/logging frameworks — all to meet the client’s need for simplified deployment, enhanced infrastructure scalability and reliability, and cost-efficiency. The result: a ten-fold scalability boost, streamlined developer workflows, and significantly optimized infrastructure spending — empowering TEN to focus on innovation and growth rather than operational complexity.

How to Integrate MLOps into Your Business Workflows?

There are three levels of MLOps implementation depending on the level of automation in your organization. Manual ML workflows and data-driven processes characterize Level 0 for organizations just starting to use machine learning systems. Each step is performed manually, including data preparation, ML training, and model performance and testing.

This process separates the aforementioned specialists who create the model from the developers who implement it. Infrequent releases mean that data science teams may retrain models only a few times a year. There are no CI/CD constraints for ML models with the rest of the application code. Similarly, there is no active performance monitoring.

Organizations that want to train the same models on new data often need to implement maturity level 1. Level 1 MLOps focuses on continuous model training by automating the ML pipeline. Your development teams, in collaboration with data scientists, create modular code components that can be reused, composed, and presumably applied collaboratively in ML pipelines. You also create a centralized function store that standardizes the storage, access, and definition of functions for ML training and serving. In addition, you can manage metadata, such as information about each stage of the pipeline and reproducibility data.

Finally, Level 2 MLOps is designed for organizations that want to experiment more and create new models more frequently that require continuous training. It is suitable for high-tech companies that update their models in minutes, retrain them hourly or daily, and deploy them across thousands of servers simultaneously. Artjoker’s specialists work with all levels thanks to their skills and awareness of the latest trends in machine learning operations.

Why Work with Artjoker Experts for MLOps Solutions?

When you partner with Artjoker, you gain access to a seasoned team of certified professionals. They blend AI-driven automation with cloud-native DevOps and agile engineering practices. With over 19 years of experience, more than 1,000 projects have been delivered worldwide.

If your goal is to reliably operationalise machine learning models, Artjoker can provide the strategic partnership, technical expertise, and delivery discipline to make it happen. Just book a free consultation ASAP, and we’ll get the best experts for your project!